基于稀疏贝叶斯学习的码元速率估计

金艳; 田田; 姬红兵

doi:10.11999/JEIT170906

基于稀疏贝叶斯学习的码元速率估计

doi: 10.11999/JEIT170906

(西安电子科技大学电子工程学院西安 710071)

基金项目:

国家自然科学基金(61201286)，陕西省自然科学基金(2014JMS304)

详细信息

作者简介:
金艳：金艳：女，1978年生，博士，副教授，硕士生导师，研究方向为现代信号处理、统计信号处理、信号参数估计、通信信号侦测等. 田田：女，1993年生，硕士生，研究方向为信号参数估计、脉冲噪声处理. 姬红兵：男，1963年生，博士，教授，博士生导师，主要研究方向为光电信息处理、微弱信号检测与识别、医学影像处理等.

中图分类号: TN911.72
计量
- 文章访问数: 1363
- HTML全文浏览量: 286
- PDF下载量: 208
- 被引次数: 0
出版历程
- 收稿日期: 2017-09-26
- 修回日期: 2018-03-14
- 刊出日期: 2018-07-19

Symbol Rate Estimation Based on Sparse Bayesian Learning

JIN Yan TIAN Tian JI Hongbing

Funds:

The National Natural Science Foundation of China (61201286), The Natural Science Foundation of Shannxi Province (2014JMS304)

摘要

摘要: 现有的相位编码信号码元速率估计方法在样本点足够多的情况下才能准确估计出参数，且算法复杂度高。针对此问题，该文详细分析了BPSK信号的结构特征，并以此为先验信息对其循环自相关(CA)向量进行压缩采样，降低了传统贝叶斯复数处理方法的维度。利用压缩传感中离散傅里叶变换矩阵的奇偶性，分解传感矩阵为正弦和余弦变换，分别将CA向量的实虚部转换到对应变换域测量，根据复数信号实虚部具有相同支撑集这一特点，采用多任务稀疏贝叶斯重构时延积向量的单边谱分量，从而估计出码元频率。理论分析和仿真结果表明，相较于其它基于稀疏贝叶斯学习的参数估计算法，所提方法在测量数量较少的情况下也能准确估计出循环频率，且算法实时性显著提高。
- 码元速率估计 /
- 稀疏贝叶斯学习 /
- 循环自相关 /
- 单边谱
Abstract: Existing methods for symbol rate estimation of phase coded signals require amounts of sensing data, and are of high computational complexity. This paper analyzes the structure characteristics of BPSK signals, which are employed as the prior information for signal compressing and dimensionality reduction. The sensing matrix can be split into sine and cosine component, combined with the Fourier transform parity. According to the fact that the real and imaginary components of a complex value share the same support set, the symbol rate estimation can be obtained, using unilateral spectral of the delay-product vector reconstructed by multi-task Bayesian compressive sensing. Theoretical analysis and simulation results show that compared with other parameter estimation algorithms, the proposed method can reduce the measurements and significantly improve the real-time ability, while keeping the high reconstruction accuracy.
- Symbol rate estimation、Sparse Bayesian learning、Cyclic autocorrelation、Unilateral spectrum /

HTML全文

参考文献(16)

[2] YILDIRIM A. Method for estimating the central frequency of phase-coded radar signals[J]. IET Signal Processing, 2017, 10(9): 1073-1081. doi: 10.1049/iet-spr.2016.0237.

JIN Yan, and JI Hongbing. Robust symbol rate estimation of PSK signals under the cyclostationary framework[J]. Circuits, Systems, and Signal Processing, 2014, 33(2): 599-612. doi: 10.1007/s00034-013-9639-7.

[3] GARDNER W. Exploitation of spectral redundancy in cyclostationary signals[J]. IEEE Signal Processing Magazine, 1991, 8(2): 14-36. doi: 10.1109/79.81007.

[4] DIKMESE S, ILYAS Z, SOFOTASIOS P C, et al. Sparse frequency domain spectrum sensing and sharing based on cyclic prefix autocorrelation[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(1): 159-172. doi: 10.1109 /JSAC.2016.2633058.

[5] KHALAF Z and PALICOT J. New Blind Free-Band Detectors Exploiting Cyclic Autocorrelation Function Sparsity[M]. Switzerland: Springer International Publishing, 2014: 91-117.

[6] BOLLIG A and MATHAR R. Dictionary-based reconstruction of the cyclic autocorrelation via l1-minimization for cyclostationary spectrum sensing[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013: 4908-4912. doi: 10.1109/ICASSP.2013.6638594.

[7] CHEN Xushan, ZHANG Xiongwei, YANG Jibin, et al. Gridless sparse reconstruction for the cyclic autocorrelation estimation[C]. IEEE International Conference on Advanced Communication Technology, Pyeongchang, South Korea, 2016: 254-259. doi: 10.1109/ICACT.2016. 7423349.

LIU Fang, WU Jiao, YANG Shuyuan, et al. Research advances on structured compressive sensing[J]. Acta Automatica Sinica, 2013, 39(12): 1980-1995. doi: 10.3724/ SP.J.1004.2013.01980.

[9] TIPPING M E. Sparse bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, 1(3): 211-244. doi: 10.1162/15324430152748236.

[10] HUANG X, ZHAO Q, JIANG W, et al. Frequency estimation of cyclic spectrum carrier based on compressive sampling of BPSK signal[C]. IET International, Radar Conference, Hangzhou, China, 2015: 1-4. doi: 10.1049/cp.2015.1373.

[11] KHALAF Z, NAFKHA A, and PALICOT J. Blind spectrum detector for cognitive radio using compressed sensing and symmetry property of the second order cyclic autocorrelation [C]. IEEE International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications, Stockholm, Sweden, 2012: 291-296. doi: 10.4108/icst. crowncom.2012.248133.

[12] WU Qisong, ZHANG Yimin D, AMIN M G, et al. Complex multitask Bayesian compressive sensing[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, 2014: 3375-3379. doi: 10.1109/ ICASSP.2014.6854226.

WANG Wei, TANG Weimin, WANG Ben, et al. Sparse signal recovery based on complex bayesian compressive sensing[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1419-1423. doi: 10.11999/JEIT151056.

ZHAO Shujie and ZHAO Jianxun. Signal Detection and Estimation Theory[M]. Beijing: Publishing House of Electronics Industry, 2013: 129-139.

[15] JI Shihao, DUNSON D, and CARIN L. Multitask compressive sensing[J]. IEEE Transactions on Signal Processing, 2009, 57(1): 92-106. doi: 10.1109/TSP.2008. 2005866.

[16] GIRI R and RAO B. Type I and Type II Bayesian methods for sparse signal recovery using scale mixtures[J]. IEEE Transactions on Signal Processing, 2016, 64(13): 3418-3428. doi: 10.1109/TSP.2016.2546231.

施引文献

资源附件(0)

访问统计